ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (2): 363-374.doi: 10.7544/issn1000-1239.2019.20170501

• 网络技术 • 上一篇    下一篇

移动蜂窝网络流量的时延特征识别方法研究

魏松杰,吴超,罗娜,张功萱   

  1. (南京理工大学计算机科学与工程学院 南京 210094) (swei@njust.edu.cn)
  • 出版日期: 2019-02-01
  • 基金资助: 
    国家自然科学基金项目(61472189);赛尔网络下一代互联网技术创新项目(NGII20160105,NGII20160601);空中交通管理系统与技术国家重点实验室开放基金项目(SKLATM201703)

Traffic Latency Characterization and Fingerprinting in Mobile Cellular Networks

Wei Songjie, Wu Chao, Luo Na, Zhang Gongxuan   

  1. (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094)
  • Online: 2019-02-01

摘要: Internet骨干网流量中,混合了来自于固网接入和3G/4G移动蜂窝网络接入的不同客户端流量.在不依赖于应用层信息和查看数据报内容的前提下,使用传统的流量分析方法和特征选择,难以将两者正确区分.通过对移动蜂窝网络通信链路技术和无线资源控制(radio resource control, RRC)机制导致IP数据报时延波动的分析建模,结合TCP/IP协议数据报的往返时延(round-trip time, RTT)计算,构建了6个与数据报时延相关的网络流量特征,用于有效区分通过3G/4G和固网接入的网络流量来源.这些特征能够针对不同网络节点接入互联网技术差异所带来的网络数据包时序分布特点进行描述和匹配.在此基础上,采用多种有监督的机器学习方法,搭建了基于网络流量的分类模型并进行交叉验证.实验结果表明:利用这些时延特征建立的流量描述与分类模型,能够有效区分移动蜂窝网络接入数据流量和固网接入数据流量,分类正确率达到92%以上,并具有良好的覆盖性与容错性.

关键词: 3G/4G, 移动蜂窝网络, 流量特征, RRC机制, 网络时延

Abstract: Internet backbone traffic is a complicated mix of various data flows initiated by clients via different network connections, including 3G/4G-based mobile cellular networks and wired broadband networks. Without examining application layer meta-data or inspecting into TCP/IP packet contents, existing network traffic analysis and characterization methods struggle in differentiating traffic flows from these two types of network connections. By studying the different kinds of link layer technics and wireless radio resource control (RRC) mechanisms, the traffic temporal characteristics are analyzed and formalized based on the packet delay variance. By making use of TCP/IP packet’s round-trip time (RTT) calculation, the experiments extract six significant network traffic features related to the packet delay, and apply them to train and test machine-learning classifiers to separate 3G/4G client traffic flows from broadband connection flows. These features focus on the transmission latency caused by a client’s first-hop Internet connection, and reveal the temporal variance of packet distribution from different link flows. Experiments with realistic dataset of mobile application traffic achieve a classification precision of more than 92% with effective traffic coverage and error resilience. The proposed method surpasses other related solutions also by relying on only the temporal distribution of flow packets without needing to inspect the packet content and encapsulation.

Key words: 3G/4G, mobile cellular network, traffic characterization, RRC mechanism, network delay

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